# Incentive Mechanism for Crowdsourcing-Based Spectrum Measurement

**DOI:**https://doi.org/10.1007/978-3-319-32903-1_213-1

## Synonyms

## Definitions

Spectrum measurement is of great importance to realize the dynamic spectrum sharing framework. To construct the spectrum database, spectrum measurement is needed which measures the power of the spectrum of desired signals. Generally, this operation is performed by dedicated equipments owned by the operators. Crowdsourcing the spectrum measurement tasks to the mobile users is a promising solution to improve the accuracy of the spectrum database in a cost-efficient way.

## Background

With the tremendous increase of wireless devices and applications, there is a common belief that we are facing a severe shortage of spectrum resource for wireless communications in the near future. Currently, the spectrum resources below 6 GHz is almost fully allocated to primary users (PUs) in a static and exclusive way, which is highly inefficient. To solve the spectrum crunch, we need a paradigm shift from the static, exclusive-use framework toward a dynamic spectrum sharing framework between PUs and secondary users (SUs). One promising solution that is being widely investigated is to employ a spectrum occupancy database, which relies on propagation models to calculate the received signal strength (RSS) at any receiver location. Unfortunately, this model-based spectrum database is prone to offer inaccurate and stale spectrum availability in many circumstances, e.g., approximately 40–70% of available white space is wasted in New York City (Saeed et al., 2014).

To improve the spectrum database accuracy, real-time spectrum measurements could be incorporated into the quasi-static database. A basic approach is to deploy dedicated sensors uniformly over the region of interest (Phillips et al., 2012). However it suffers from the high cost for database operator. A practical alternative is to exploit crowdsourcing for the sensing task (Nika et al., 2014), i.e., recruiting users with mobile devices that are outfitted with spectrum sensors. However participating in a crowdsourcing task may incur additional bandwidth usage and energy consumption for mobile users, and thus rewards in the form of either money or resource are needed to encourage them to make contributions.

Incentive mechanism for crowdsourcing-based spectrum measurement has been firstly investigated by Ying et al. (2015). Their goal is to minimize the interpolation variance for all the spots of interest for a given budget. Both budget-free mechanism with a cardinality constraint and budget-feasible mechanism by using bisection method are proposed. Gao et al. (2016) proposed a game-theoretic model-based mechanism to incentivize the users with additional spectrum access opportunities. Specifically, a two-level game model is considered, in which the database conducts dynamic pricing in a first-level Stackelberg game and SUs strategically contribute to spectrum sensing in a second-level stochastic game. One limitation of these mechanisms (Ying et al., 2015; Gao et al., 2016) is that they do not take into consideration the heterogeneity of spots that needs to be augmented and thus cannot recruit users to meet distinct interpolation requirements. In Wang et al. (2017b) proposed a fine-grained incentive mechanism for sensing augmented spectrum database by utilizing auction model Wang et al. (2017a), where users sell their location-specific spectrum measurement to the database operator. Instead of trying to augment the whole spectrum database, the spectrum sensing is only conducted on spots with poor propagation model-based estimation. In Wang et al. (2017c) proposed a barter-like exchange model to incentivize the SUs by using spectrum access right. Based on this model, a truthful reverse auction approach is adopted to select the SUs and determine the individual access time in a computational efficient way. In Chen et al. (2017) proposed a reputation-based cooperative spectrum sensing incentive framework, where the cooperation stimulation problem is modeled as an indirect reciprocity game. In the proposed framework, SUs choose how to report their sensing results to the database center and gain reputations, based on which they can access a certain amount of vacant licensed channels in the future.

## Crowdsourcing Augmented Spectrum Database

*x*

_{i},

*y*

_{i}) is

*z*

_{i}=

*z*(

*x*

_{i},

*y*

_{i}). Given the values at a set of locations \(\mathcal {N}=\{(x_1,y_1)\), (

*x*

_{2},

*y*

_{2}), …, (

*x*

_{n},

*y*

_{n})}, Kriging could predict the unknown value at a new location \(\hat {(z_0)}\) from the weighted known values as follows:

*λ*

_{i}is the normalized weight, i.e., \(\sum _{i=1}^{n} {\lambda _i}=1\). The optimal weights

*λ*

_{i}are determined by minimizing the estimation variance, i.e.:

*z*

_{i}is intrinsically stationary such as:

*x*

_{0},

*y*

_{0}) by using the measurements at set \(\mathcal {N}\). By substituting Eq. (1) into it, the following formula could be obtained:

*C*

_{ij}=

*Cov*(

*z*

_{i},

*z*

_{j}) and

*Cov*() denotes the covariance function.

*semivariogram*

*γ*

_{ij}, is introduced.

*γ*

_{ij}models the variance between two points as a function of their distance. The theoretical semivariogram is represented by

*γ*

_{ij}could be expressed as

*γ*

_{ij}as

*λ*

_{i}could be obtained if semivariogram

*γ*

_{ij}is known. In practice,

*γ*

_{ij}is estimated from measurements and then fitted with empirical curve, e.g., exponential or spherical model. The minimized \(\phi _{(x_0,y_0)}(\mathcal {N})\) represents the estimation uncertainty at an unmeasured location (

*x*

_{0},

*y*

_{0}) by using measurements at set \(\mathcal {N}\), which could be used as a criterion in incentive mechanism design.

## Incentive Mechanism

In this subsection, we introduce two kinds of incentive mechanisms for crowdsourcing-based spectrum measurement by using monetary reward and barter-like resource exchange, respectively. The considered network consists of a set of mobile users \(\mathcal {N}\) with user index *i*, who knows its current location (*x*_{i}, *y*_{i}). The set of spots that needs to be augmented (i.e., interpolated) by sensing results is denoted by \(\mathcal {M}\) with spot index *j*. The result of interpolation is quantified by the Kriging estimation variance, e.g., the estimation variance at spot *j* by the interpolation of the user set \(\mathcal {N}\) is denoted as \(\phi _j(\mathcal {N})\). The spectrum database acquires the sensing data from users periodically. The set of spots that needs to be interpolated varies at each period. At the beginning of a period, the spectrum database announces a sensing request with the center frequency information. Notice that the request does not need to contain the location information of the spots. Upon receiving the request, interested user *i* competes for this crowdsourcing task by submitting its location information (*x*_{i}, *y*_{i}). The database selects a winner set \(\mathcal {W}\) (\(\mathcal {W} \subseteq \mathcal {N}\)) to perform the crowdsourcing, and accordingly the informed winners report their sensing measurements to the database.

### Monetary Reward-Based Incentive Mechanism

*cost*

*c*

_{i}occurring to user

*i*, which is related to the additional bandwidth usage and energy consumption. Notice that the cost is a private information and thus is only known by the user itself. If user could receive a monetary reward

*p*

_{i}from the database which is higher than its cost

*c*

_{i}, user

*i*would have the incentive to participate in the crowdsourcing task. Therefore, based on the assumption that the users are rational, they are always trying to maximize their utility which could be defined as follows:

*j*may have an estimation quality requirement

*r*

_{j}.

*r*

_{j}could be quantified by the Kriging estimation variance, that is, spot

*j*’s RSS needs to be interpolated with a maximum estimation variance

*r*

_{j}. Therefore, the considered problem could be formulated as the following optimization problem:

*j*by the interpolation of the winner set \(\phi _j(\mathcal {W})\) is no larger than

*j*’s requirement. The considered minimization problem is equivalent to the subset selection problem, which is proven to be NP-hard. Therefore, it is impossible to compute the optimal set of selected users that minimizes the total costs in polynomial time.

*b*

_{i}to the database, which may or may not be its real cost

*c*

_{i}. The mechanism consists of two phases:

*winner selection*and

*payment*

*determination*. Winner selection algorithm is based on the greedy heuristics that keeps selecting the next user with most

*cost-efficient contribution*until all the spots’ requirements are met. The cost-efficient contribution of user

*i*changes according to the current winner set \(\mathcal {W}\), which is defined as

*b*

_{i}is

*i*’s bid and \(m_i(\mathcal {W})\) is

*i*’s

*total weighted marginal contribution*based on the current \(\mathcal {W}\). \(m_i(\mathcal {W})\) is calculated by

*i*to spot

*j*under the current winner set \(\mathcal {W}\). And this marginal contribution is weighted by 1∕

*r*

_{j}, i.e., the spot with small variance requirement has large weight.

*b*

_{i}=

*c*

_{i}) is the dominant strategy regardless of other users’ strategies. Specifically, to find the critical payment for winner

*k*, the payment determination algorithm repeats the winner selection algorithm for the user set without

*k*, i.e., \(\mathcal {N}'=\mathcal {N}\setminus \{k\}\). In set \(\mathcal {N}'\), the mechanism finds the winner

*l′*who has the maximum \(\alpha _{l'}(\mathcal {W'})\) based on the current winner set \(\mathcal {W'}\). To let winner

*k*replace winner

*l′*, its cost-efficient contribution needs to be larger than that of

*l′*, that is:

*k*that lets it replace winner

*l′*is \(\frac {m_k(\mathcal {W'})}{m_{l'}(\mathcal {W'})}\cdot b_{l'}\). Eventually, the maximum of these values is used as the critical payment for

*k*.

### Barter-Like Resource Exchange-Based Incentive Mechanism

*p*

_{i}which is in the form of additional channel access time. And each user that participates the crowdsourcing task has a desired channel access time, which is denoted by

*t*

_{i}. Notice that

*t*

_{i}is a private information and thus is only known by the user itself. The length of each round that the database operator wants to collect new measurements is

*T*, during which the selected users could share the channel access in a TDMA fashion. The rational user always tries to maximize its utility, which is defined as

*T*. Similar to Eq. (), this optimization problem is NP-hard, and an auction-based mechanism could solve it in an efficient way (Wang et al., 2017c).

*contribution-bidding ratio*until the total bids exceed the current

*bidding threshold*. The bidding threshold is related to the time interval

*T*, which could be found by employing a bisection searching method. The contribution-bidding ratio of user

*i*based on the current winner set \(\mathcal {W}\) is defined as

*b*

_{i}is

*i*’s bid and \(c_i(\mathcal {W})\) is

*i*’s

*interpolation variance contribution*based on the current \(\mathcal {W}\). \(c_i(\mathcal {W})\) is calculated by

*k*, the winner selection algorithm for the user set without

*k*is repeated, i.e., \(\mathcal {N}'=\mathcal {N}\setminus \{k\}\). In set \(\mathcal {N}'\), the winner

*k′*could be found who has the maximum \(\alpha _{k'}(\mathcal {W'})\) based on the current winner set \(\mathcal {W'}\). To let winner

*k*replace winner

*k′*, its contribution-bidding ratio needs to be larger than that of

*k′*, that is:

*k*that lets it replace winner

*k′*is \(\frac {c_k(\mathcal {W'})}{c_{k'}(\mathcal {W'})}\cdot b_{k'}\). Eventually, after finishing the winner selection loop on user set \(\mathcal {N}'\), the maximum of these values is used as the critical payment for

*k*.

In a truthful auction, generally the final total payments are larger than the sum of winners’ bids. The final total payment is constrained by the time interval *T*. However, it is impossible to use constraint *T* to control the winner selection process, since the payment is determined after the selection of winners. Therefore, it is required to define a bidding threshold *H* to control the winner selection loop, i.e., the sum of the bids for all winners should not exceed this threshold. Obviously, the bidding threshold is smaller than the time constraint *T*, and using a bisection searching method could find it. Specifically, a lower bound *l* and a upper bound *u* are used to narrow down the possible range of the bidding threshold, and a tolerance *e* is employed to stop the searching process.

## Key Applications

The crowdsoucing-based spectrum measurements are crucial for the dynamic spectrum sharing. Due to the tremendous increasing of mobile data traffic, dynamic and efficient spectrum sharing mechanism is required. To realize interference protection and reuse the whitespace efficiently, an accurate spectrum database that using crowdsourced spectrum measurement is promising.

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